Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms

In this chapter, we discuss the use of multiobjective evolutionary algorithms (MOEAs) for solving single-objective optimization problems in dynamic environments. Specifically, we investigate the consideration of a second (artificial) objective, with the aim of maintaining greater population diversity and adaptability. The paper suggests and compares a number of alternative ways to express this second objective. An empirical comparison shows that the best alternatives are competitive with other evolutionary algorithm variants designed for handling dynamic environments.

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